Quantitative Economics

Journal Of The Econometric Society

Edited by: Stéphane Bonhomme • Print ISSN: 1759-7323 • Online ISSN: 1759-7331

Quantitative Economics: May, 2023, Volume 14, Issue 2

Bootstrap inference under cross-sectional dependence

p. 511-569

Timothy G. Conley, Sílvia Gonçalves, Min Seong Kim, Benoit Perron

In this paper, we introduce a method of generating bootstrap samples with unknown patterns of cross‐ sectional/spatial dependence, which we call the spatial dependent wild bootstrap. This method is a spatial counterpart to the wild dependent bootstrap of Shao (2010) and generates data by multiplying a vector of independently and identically distributed external variables by the eigendecomposition of a bootstrap kernel. We prove the validity of our method for studentized and unstudentized statistics under a linear array representation of the data. Simulation experiments document the potential for improved inference with our approach. We illustrate our method in a firm‐level regression application investigating the relationship between firms' sales growth and the import activity in their local markets using unique firm‐level and imports data for Canada.

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Supplemental Material

Supplement to "Bootstrap inference under cross sectional dependence"

Timothy Conley, Sílvia Gonçalves, Min Seong Kim, and Benoit Perron